Engineer Sr, Applied Research Science

Shureorporated•Niles, IL

6d•Hybrid

About The Position

Join Shure’s Signal Processing and Applied Research team to explore the future of intelligent audio. The Senior Engineer in Applied Research Science researches and develops innovative AI/ML and advanced algorithmic solutions to address complex, real-world customer challenges. Working with software, signal processing, data, and test engineers, this role investigates emerging technologies, develops and demonstrates new approaches, and helps move research and experimentation toward product innovation. The position is hybrid and based at Shure’s Niles, Illinois headquarters. The role focuses on audio applications, including single- and multi-channel audio processing and speech enhancement. It includes researching and training models, building prototypes, optimizing solutions for latency and computational efficiency, evaluating results using objective metrics and listening tests, and creating robust training and test datasets. The engineer also documents research, supports shared and repeatable engineering practices, keeps current with developments in industry and academia, and contributes to intellectual property and technical working groups. Applicants must be currently authorized to work in the United States on a full-time basis. Shure will not sponsor applicants for work visas.

Requirements

Bachelor’s degree in electrical engineering, computer science, mathematics, statistics, physics, data science, machine learning, or a related research science field, with at least 5 years of related experience; or a master’s degree in one of these fields with at least 3 years of related experience; or a PhD in one of these fields with no work experience required.
Proficiency in Python.
Proficiency with frameworks and libraries such as PyTorch, TensorFlow, scikit-learn, NumPy, and Matplotlib.
Proficiency with tools and technologies including Git/GitHub, Docker, Jupyter, AWS, and on-premises GPU training tools.
Current authorization to work in the United States on a full-time basis; visa sponsorship is not available.

Nice To Haves

C/C++ or MATLAB programming experience.
Experience in audio classification and scaling audio classification frameworks for efficient development and maintenance.
Experience using AI-assisted software development and research tools, such as GitHub Copilot, agentic AI systems, large language models, and code generation and analysis tools.
Experience independently deploying, validating, and demonstrating early machine-learning proofs of concept.
Knowledge or experience in classical digital signal processing.
Proficiency in developing low-latency, embedded-friendly solutions.
Audio engineering, recording, or other audio production experience, including use of DAWs.

Responsibilities

Collaborate with cross-functional teams to research, design, and implement audio features and products.
Identify valuable AI/ML use cases with colleagues, stakeholders, and engineering partners, and explore ways to optimize, scale, and platform solutions.
Review academic and industry literature and conduct original research and experimentation to address complex technical problems; share findings, insights, and prototypes with colleagues and leaders.
Work with engineers and product managers to define and document performance metrics, system constraints, technical requirements, and architectural options.
Design and train custom machine learning models and algorithms for audio applications, including single- and multi-channel audio processing and speech enhancement.
Optimize models for latency, computational efficiency, and deployment requirements in partnership with software engineers.
Evaluate models and algorithms against performance metrics, refine solutions, and conduct subjective listening tests when appropriate.
Develop prototypes using machine learning and advanced DSP techniques, addressing real-time, low-latency data pipelines and computational constraints.
Identify, acquire, collect, and generate relevant data for robust training and test datasets, including licensed or purchased data, internally collected data, and simulations.
Support the creation of high-quality datasets using internal annotation tools and third-party annotation partners.
Apply and strengthen machine learning software engineering practices, including shared toolkits, code repositories, and experiment tracking.
Monitor developments in industry and academia, participate in relevant training and conferences, and identify opportunities to incorporate emerging research and techniques.
Contribute to collaborative technical working groups, including work beyond the role’s primary area of expertise.
Document research methods, experiments, findings, results, and technical insights using collaborative documentation tools.
Contribute to intellectual property development and technical brainstorming, and foster experimentation and innovation.

Benefits

Comprehensive healthcare
Mental health plans
Retirement savings plans
Generous paid time off programs
Employee discounts
Professional development opportunities
Work-life balance initiatives
Employee recognition programs
Volunteering and community involvement opportunities
Competitive rewards packages addressing physical, mental, and financial well-being